About Muhammad
AWS Certified Solutions Architect with 3 years of hands-on AWS experience and 8+ years overall in software engineering, machine learning, and cloud- native development. Proven success designing event-driven and serverless systems, leading infrastructure automation with AWS CDK, and guiding digital transformation for enterprise clients. Passionate about aligning cloud technologies with real business outcomes.
English
Native or bilingual
Remote only
Primarily works remotely
Experience
- Siemens EnergySolution ArchitectENERGY AND UTILITIESAugust 2022 - Today (4 years)Finspång, OG, Sweden• Architected the Document Processing & OCR Platform, scaling to ~4-5k documents/day for 1000+ users; introduced token-based throttling (SQS FIFO + Step Functions) that eliminated subnet IP-exhaustion incidents.• Designed a multi-tier, event-driven ChatGPT-for-Docs service (API Gateway Lambda ' ECS) supporting 3000+ 'employees while remaining compliant with corporate security policies.• Led backend & SAP integration for Al-Driven Material Pricing; introduced an AWS AppConfig-based region template-adding a new sales region now takes a JSON update, after which the platform auto-provisions and scales, lifting price-estimate accuracy by 15%• Drove company-wide adoption of AWS CDK; cut environment provisioning time and enabled repeatable deployments.
- Siemens EnergyData ScientistAugust 2021 - August 2022 (1 year)Finspång, OG, Sweden• Implemented a Bayesian regression model for Al-Driven Material Costing, producing probabilistic price estimates that improved forecast confidence and laid groundwork for the later Al-Driven Material Costing service.• Built an NLP recommendation engine that cut part-search time by 20% and boosted process efficiency by 10%.• Developed rule based turbine signal-correction algorithms, increasing data quality for predictive-maintenance analytics.
- Siemens EnergyMaster ThesisJanuary 2021 - July 2021 (6 months)Finspång, OG, Sweden
- Created a computer‑vision pipeline for apparel classification, reducing misclassification from 40 % to 10 %.
- DesignedaCNN‑basedanomaly‑detectionmodelinTensorFlow,improvingprediction precision by 25 %
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Education
- M.S., STATISTICS & MACHINE LEARNINGLinköping University2021M.S., STATISTICS & MACHINE LEARNING
- B.S. ComputerGhulam Ishaq Khan Institute of Engineering Sciences and Technology2015B.S. Computer
Certifications
- AWS Certified Solutions Architect – ProfessionalAWS2025